Advances in AI-Based Land Use and Land Cover Classification: A Review of Deep Learning and Remote Sensing Integration

Rimsha Jamil Ghilzai; Muhammad Abubakar Siddique; Sana Rubab; Kishwar Rasool

Authors

Rimsha Jamil Ghilzai Ghazi University D.G.Khan
Muhammad Abubakar Siddique Ghazi University D.G. Khan. Pakistan
Sana Rubab Ghazi University D.G. Khan. Pakistan
Kishwar Rasool Ghazi University D.G. Khan. Pakistan

Keywords:

Land Use and Land Cover (LULC), Remote Sensing, Artificial Intelligence (AI), Deep Learning, Machine Learning, Satellite Imagery, Image Classification

Abstract

The integration of Artificial Intelligence (AI) with remote sensing has transformed Land Use and Land Cover (LULC) classification, enabling more accurate, efficient, and scalable environmental monitoring. This review synthesizes recent advancements in AI-driven LULC classification, with a focus on deep learning, transfer learning, hybrid approaches, and explainable AI (XAI). Recent studies demonstrate that AI techniques significantly enhance classification accuracy and adaptability across diverse geospatial datasets, supporting applications such as urban expansion monitoring, ecological assessment, reforestation analysis, and real-time land management. Despite these advancements, challenges remain regarding spectral resolution, model interpretability, computational efficiency, and data scarcity. This review highlights these limitations and discusses emerging solutions, including multimodal data fusion, lightweight AI models, and scalable MLOps frameworks. The findings provide insights for researchers, practitioners, and policymakers to guide future work in sustainable land management and environmental monitoring.

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